AuditPCH: Auditable Payment Channel Hub With Privacy Protection

Anonymous Payment Channel Hub (PCH), one of the most promising layer-two solutions, settles the scalability issue in blockchain while guaranteeing the unlinkability of transacting parties. However, such developments bring conflicting requirements, i.e., hiding the sender-to-receiver relationships from any third party but opening the relationship to the auditor. Existing works do not support these requirements simultaneously since off-chain transactions are not recorded in the blockchain. Further, the privacy protection strategies hinder auditors from capturing the payment relationships. Thus, it is still a challenge to audit the finance activities of PCH transacting parties. This paper proposes a novel anonymous PCH solution called AuditPCH to achieve privacy and auditability. Concretely, we design a Linkable Randomizable Puzzle scheme for constructing conditional transactions, allowing a sender to pay for a receiver via the hub. As such, AuditPCH, with the new LRP scheme, ensures that 1) payment relationships can be protected from the hub and 2) an auditor with necessary trapdoors can associate the sender and receiver of a payment. We prove the security of AuditPCH under the Global Universal Composability framework. The extensive experimental evaluations on AuditPCH are established to demonstrate its functionality and flexibility.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Information Forensics and Security
Published
2024-12-11
DOI
https://doi.org/10.1109/tifs.2024.3515820
Citations
3
Primary Topic
IPv6, Mobility, Handover, Networks, Security
Type
article
Field-Weighted Citation Impact
0.63
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AuditPCH: Auditable Payment Channel Hub With Privacy Protection

Robert H. Deng, Junzuo Lai, Jiahe Wu, Pengfei Wu et al.
3 citations
IEEE Transactions on Information Forensics and Security
IPv6, Mobility, Handover, Networks, Security
0.63
article

AuditPCH: Auditable Payment Channel Hub With Privacy Protection

Robert H. Deng, Junzuo Lai, Jiahe Wu, Pengfei Wu, Yingjiu Li, Jianfei Sun, Jian Weng, Ming Li, Yuxian Li
article en
3 citations

Abstract

Anonymous Payment Channel Hub (PCH), one of the most promising layer-two solutions, settles the scalability issue in blockchain while guaranteeing the unlinkability of transacting parties. However, such developments bring conflicting requirements, i.e., hiding the sender-to-receiver relationships from any third party but opening the relationship to the auditor. Existing works do not support these requirements simultaneously since off-chain transactions are not recorded in the blockchain. Further, the privacy protection strategies hinder auditors from capturing the payment relationships. Thus, it is still a challenge to audit the finance activities of PCH transacting parties. This paper proposes a novel anonymous PCH solution called AuditPCH to achieve privacy and auditability. Concretely, we design a Linkable Randomizable Puzzle scheme for constructing conditional transactions, allowing a sender to pay for a receiver via the hub. As such, AuditPCH, with the new LRP scheme, ensures that 1) payment relationships can be protected from the hub and 2) an auditor with necessary trapdoors can associate the sender and receiver of a payment. We prove the security of AuditPCH under the Global Universal Composability framework. The extensive experimental evaluations on AuditPCH are established to demonstrate its functionality and flexibility.

IEEE Transactions on Information Forensics and SecurityVol. 20
Jinan University (CN), University of Oregon (US), Singapore Management University (SG)
Openalex Percentile: Top 30%
IPv6, Mobility, Handover, Networks, Security
0.63
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.